Method and system for secretion analysis embedded in a garment

ABSTRACT

An apparatus, and method, for a garment embedded secretion analysis. The system includes a liner that includes at least a sensor from a plurality of sensors. The system also includes a computing device embedded in the liner and communicatively connected to the at least a sensor, where the computing device includes a detection module configured to extract at least a biological sample from the user, authenticate the user as a function of the biological sample and a biological data of the user, detect a condition datum as a function of the biological sample and biological data of the user and determine an event datum as a function of the condition datum. Computing device also includes a safety module configured to receive the event datum and generate an alert datum as a function of the event datum.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalPatent Application Ser. No. 63/354,070, filed on Jun. 21, 2022, andtitled “A SMART UNDERWEAR GARMENT SYSTEM,” which is incorporated byreference herein in its entirety.

FIELD OF THE INVENTION

The present invention generally relates to the field of secretionanalysis. In particular, the present invention is directed to monitoringsafety and health of a user through a device embedded in garment.

BACKGROUND

Smart technology in clothing garments can help alleviate harm as well asregulate the body of a person. Smart technology in clothing may help toprevent future harm through detection of harmful elements in person'sbody and alerting the user of the presence of the harm.

SUMMARY OF THE DISCLOSURE

In an aspect an apparatus for a garment embedded secretion analysis. Theapparatus including a liner that includes at least a sensor from aplurality of sensors. The system also includes a computing deviceembedded in the liner and communicatively connected to the at least asensor, where the computing device includes a detection moduleconfigured to extract at least a biological sample from the user,authenticate the user as a function of the biological sample and abiological data of the user, detect a condition datum as a function ofthe biological sample and biological data of the user and determine anevent datum as a function of the condition datum. Computing device alsoincludes a safety module configured to receive the event datum andgenerate an alert datum as a function of the event datum.

In another aspect a method for a garment embedded secretion analysis.The method includes extracting, by a computing device communicativelyconnected to at least a sensor embedded in a liner, at least abiological sample from a user. The method also includes authenticating,by the computing device, the user as a function of the at least abiological sample and biological data of the user, and detecting, by thecomputing device, a condition datum as a function of the biologicalsample and biological data of the user. The method also includesdetermining, by the computing device, an event datum as a function ofthe condition datum and generating, by the computing device, an alertdatum as a function of the event datum.

In another aspect a method of manufacturing a garment embedded secretionanalysis system. The method of manufacturing includes collecting afabric to comprise a liner for the garment, weaving conductive yarn intothe fabric of the liner, embedding at least a sensor from a plurality ofsensors into the fabric of the liner, and installing a computing deviceinto the liner.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of agarment embedded secretion analysis system;

FIG. 2A shows a front view of an exemplary embodiment of a garmentembedded secretion analysis system;

FIG. 2B shows a back view of an exemplary embodiment of a garmentembedded secretion analysis system;

FIG. 2C shows a front side of an illustrative embodiment of a garmentembedded secretion analysis system;

FIG. 2D is an exemplary embodiment of a liner;

FIG. 3 is an illustrative embodiment of a biological sample database;

FIG. 4 is a block diagram of an exemplary embodiment of amachine-learning module;

FIG. 5 is a block diagram illustrating an exemplary embodiment of aneural network;

FIG. 6 is a diagram of an exemplary embodiment of a node of a neuralnetwork;

FIG. 7 is a diagram of an exemplary embodiment of a fuzzy setcomparison;

FIG. 8 is a flow diagram illustrating an exemplary embodiment of amethod of manufacturing for a garment embedded secretion analysissystem;

FIG. 9 is a flow diagram illustrating a garment embedded secretionanalysis method; and

FIG. 10 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for garment embedded secretion analysis system. Inan embodiment, a computing device includes a detection module thatextracts a biological sample from a user, authenticate the user based onthe biological sample and biological data of the user, detects acondition datum based on the biological sample and biological data ofthe user, and determine an event datum based on the condition datum. Thecomputing devices also includes a safety module that receives the eventdatum from detection module and generates an alert datum based on theevent datum.

Aspects of the present disclosure can be used to monitoring fertility ofa user. Aspects of the present disclosure can also be used to alert userand others of the presence of dangerous elements in user's body, such aselements known as rape drugs. This is so, at least in part, becausesystem is configured to detect the presence of the element in abiological sample, determine that this element is a specific rape-drugand generate an alerting warning user and others of the possibleinvoluntary intoxication, which may enable user to seek help and otherto help the user.

Aspects of the present disclosure allow for transmitting a user'slocation, based on GPS, when danger is detected. Exemplary embodimentsillustrating aspects of the present disclosure are described below inthe context of several specific examples.

Referring now to FIG. 1 , an exemplary embodiment of a apparatus 100 forsecretion analysis embedded in a garment is illustrated. Apparatus 100includes a liner 104. A “liner,” as used throughout this disclosure, isan extra piece of fabric that lines a garment with the purpose ofabsorbing bodily fluids. As used herein, “bodily fluids” are liquidsheld that may be expelled and/or excreted from a human body. Bodilyfluids may include, without limitation, sweat, urine, mucus, blood,menstrual blood, saliva, fecal matter, semen, or vaginal fluids such asdischarge. Moreover, liner 104 may comprise an absorbent material tocapture these bodily fluids. Absorbent materials used may be, butwithout limitation, cellulose, natural fibers, microfibers, absorbentgel material, or any other material configured to absorb fluid. In someembodiments, liner 104 may also be made of non-absorbent material. Insome embodiments, liner 104 may be configured to change color based othe presence of certain biomarkers, such as increased salt level,presence of protein in urine (proteinuria), or/and hormones, such as ahuman chorionic gonadotropin (hCG). In a nonlimiting example, liner 104may be configured to change to a blue color when droplets of urine thatcome in contact with liner 104 contains high levels of a pregnancyrelated hormone, such as the hCG hormone.

Continuing to refer to FIG. 1 , liner 104 includes at least a sensor 108from a plurality of sensors. As used herein, a “sensor” is a device,module, and/or subsystem, utilizing any hardware, software, and/or anycombination thereof to detect events and/or changes in the instantenvironment and transmit the information; transmission may includetransmission of any wired or wireless electronic signal. At least asensor 108 may include any electromagnetic sensor, including withoutlimitation electroencephalographic sensors, magnetoencephalographicsensors, electrocardiographic sensors, electromyographic sensors, or thelike. At least a sensor 108 may include a temperature sensor. At least asensor 108 may include any sensor that may be included in a mobiledevice and/or wearable device, including without limitation a motionsensor such as an inertial measurement unit (IMU), one or moreaccelerometers, one or more gyroscopes, one or more magnetometers, orthe like. At least a wearable and/or mobile device sensor may capturestep, gait, and/or other mobility data, as well as data describingactivity levels and/or physical fitness. At least a sensor 108 maydetect heart rate or the like. At least a sensor 108 may detect anyhematological parameter including blood oxygen level, pulse rate, heartrate, pulse rhythm, blood sugar, and/or blood pressure. At least asensor 108 may be configured to detect internal and/or externalbiomarkers and/or readings. At least a sensor 108 may be a part ofsystem 100 or may be a separate device in communication with apparatus100. Sensor or at least a sensor 108 are used interchangeably throughoutthis disclosure. At least a sensor 108 may include a volatile organiccompound (VOC) sensor, such as a chemical and/or an electrochemicalsensor. In a nonlimiting example, VOC sensor may detect increased levelsof ketone in a user's biological sample, where the increased levels ofketone may mean that the user is undergoing some form of physiologicalstress. In another non-limiting example, sensor 108 may detect that useris undergoing some physiological stress, such as being robbed atgunpoint, by detecting a spike in user's heart rate while detecting thatthe user is not performing any movement that would correlate to theelevated heart rate. VOC sensor may include, without limitations, theSD-MSS-1K2GP sensor, made by NanoWorld AG, located at rue des Saars102000 Neuchatel, Switzerland.

With continued reference to FIG. 1 , at least a sensor 108 may beconfigured to measure body temperature. In this disclosure, “bodytemperature” is the measure of the internal hotness or coldness of thebody of the user. Temperature, for the purposes of this disclosure, andas would be appreciated by someone of ordinary skill in the art, is ameasure of the heat energy of a system, or in this case, the body of theuser. Temperature, as measured by any number or combinations of sensorspresent within sensor suite, may be measured in Fahrenheit (° F.),Celsius (° C.), Kelvin (° K), or another scale alone or in combination.The temperature measured by sensors may comprise electrical signalswhich are transmitted to their appropriate destination wireless orthrough a wired connection. Additionally, at least a sensor 108 isfurther configured to measure a property of a bodily fluid. As usedherein, a “property” of a bodily fluid refers to a quality or trait of abodily fluid, especially one that's peculiar or out of place compared tonormal. A property of bodily fluids may include any sort of measurement,density, viscosity, surface tension, volume, weight, presence of otherfluids, or anything similar. For example, a property of a bodily fluidmay include measuring a fertile period by measuring the beginning andend of menstrual blood. Another example of a property of a bodily fluid,without limitation, is the concentrations of components in sweat.Components may include but not limited to urea, uric acid, ammonia,lactic acid, or vitamin C. Another property of a bodily fluid mayinclude viscosity of sweat or even the volume of sweat. Additionally,another property may include, without limitation, the presence of bloodin urine. At least a sensor 108 in the plurality of sensors may beconfigured to include an agent that may change color as a function ofthe acidity, or the pH, of the urine or another bodily fluid. As usedherein, an “agent” is a chemical substance that interacts with a bodilyfluid to make a reaction. For example, if urine is above a certain pH,it may appear blue or another color on liner 104. Another example of anagent used may be, without limitation, that if sweat is absorbed byliner 104 an agent emits a scent to cover up the potential stench thesweat may cause.

Still referring to FIG. 1 , garment embedded secretion analysisapparatus 100 may be wearable. In some embodiments, Garment embeddedsecretion analysis apparatus 100 may include a wearable technology. Forthe purposes of this disclosure, a “wearable technology” is atechnological device, such as a computing device and/or processor, whichis designed to be worn by a user. For example, wearable technology mayinclude a smart watch. A smartwatch may include, as a non-limitingexample, an IWATCH. In some embodiments, wearable technology may includea GPS tracker, a GPS key fob, a fitness tracker, and the like. As anon-limiting example, a fitness tracker may include a FITBIT. In anembodiment, liner may provide geolocation data about a user and may beconfigured to provide tracking data. In an embodiment, geolocation datamay be shared with a third party such as a family member and/or friendwho may be concerned about a user's location.

Continuing to refer to FIG. 1 , garment embedded secretion analysisapparatus 100 includes a computing device 112 embedded in the liner 104and communicatively connected to at least a sensor 108. Computing device112 may be located inside, next to, be embedded in, or any other way tobe communicatively connected to at least a sensor 108 in order to detectdata. Computing device 112 may include any computing device as describedin this disclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Computing device may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Computing device 112 may include a singlecomputing device operating independently, or may include two or morecomputing device operating in concert, in parallel, sequentially or thelike; two or more computing devices may be included together in a singlecomputing device or in two or more computing devices. Computing device112 may interface or communicate with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting computing device112 to one or more of a variety of networks, and one or more devices.Examples of a network interface device include, but are not limited to,a network interface card (e.g., a mobile network interface card, a LANcard), a modem, and any combination thereof. Examples of a networkinclude, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Computing device 112 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Computing device 112 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Computing device 112 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Computing device 112 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofapparatus 100 and/or computing device.

With continued reference to FIG. 1 , as used in this disclosure,“communicatively connected” means connected by way of a connection,attachment or linkage between two or more relata which allows forreception and/or transmittance of information therebetween. For example,and without limitation, this connection may be wired or wireless, director indirect, and between two or more components, circuits, devices,systems, and the like, which allows for reception and/or transmittanceof data and/or signal(s) therebetween. Data and/or signals therebetweenmay include, without limitation, electrical, electromagnetic, magnetic,video, audio, radio and microwave data and/or signals, combinationsthereof, and the like, among others. A communicative connection may beachieved, for example and without limitation, through wired or wirelesselectronic, digital or analog, communication, either directly or by wayof one or more intervening devices or components. Further, communicativeconnection may include electrically coupling or connecting at least anoutput of one device, component, or circuit to at least an input ofanother device, component, or circuit. For example, and withoutlimitation, via a bus or other facility for intercommunication betweenelements of a computing device. Communicative connecting may alsoinclude indirect connections via, for example and without limitation,wireless connection, radio communication, low power wide area network,optical communication, magnetic, capacitive, or optical coupling, andthe like. In some instances, the terminology “communicatively coupled”may be used in place of communicatively connected in this disclosure.

Continuing to refer to FIG. 1 , at least a sensor 108 may be attached,mechanically connected, and/or communicatively connected, as describedabove, to computing device 112. For example, and without limitation, atleast a sensor 108 may include any type of sensor needed to detect thedata as described herein. For example, at least a sensor 108 may includea temperature sensor, a fluid sensor, and/or the like. At least a sensor108 may include one or more temperature sensors, which may function tosense temperature of bodily fluids or the internal temperature of theuser's body. A temperature sensor may include without limitation one ormore sensors used to detect ambient temperature or barometric pressure.Additionally, or alternatively, plurality of sensors may include ageospatial sensor. Plurality of sensors may be located inside liner 104;and/or be included in and/or attached to at least a portion of liner104. Plurality of sensors may be used to monitor the status of bodilyfluids of the user. At least a sensor 104 may be incorporated intogarment embedded secretion analysis apparatus 100 or be remote.Plurality of sensors may be communicatively connected to an energysource and/or motor. Plurality of sensors may comprise anelectrocardiogram (ECG). As used herein and throughout, an “ECG” is arecording of the heart's electrical activity. At least a sensor 108 maybe configured to detect the user's heartbeat through liner 104.

Still referring to FIG. 1 , in some embodiments, at least a sensor 108include be a strain gauge sensor. A “strain gauge sensor,” as describedherein, is a sensor configured to measure electrical resistance based onchanges in strain, where a positive strain is the result of stretching amaterial and negative strain is the result of compression. In anembodiment, strain gauge sensor is attached to the liner using asilicone film. In some embodiments, sating gauge sensor is a knittedstrain sensor. A “knitted strain sensor,” as used herein, refers to thestrain sensor that is attached to flexible electrically conductivematerial knitted with the fabric of the liner, where the strain gaugesensor is configured to detect bending deformation of the flexibleelectrically conductive material through the change of electricalsignals in the flexible electrically conductive material. In anonlimiting example, flexible electrically conductive material mayinclude a silver coated yarn thread.

With continued reference to FIG. 1 , computing device 112 may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, computingdevice 112 may be configured to perform a single step or sequencerepeatedly until a desired or commanded outcome is achieved; repetitionof a step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Computing device 112 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

Referring still to FIG. 1 , computing device 112 may be communicativelyconnected to a database. Database may be implemented, withoutlimitation, as a relational database, a key-value retrieval databasesuch as a NOSQL database, or any other format or structure for use as adatabase that a person skilled in the art would recognize as suitableupon review of the entirety of this disclosure. Database mayalternatively or additionally be implemented using a distributed datastorage protocol and/or data structure, such as a distributed hash tableor the like. Database may include a plurality of data entries and/orrecords as described above. Data entries in a database may be flaggedwith or linked to one or more additional elements of information, whichmay be reflected in data entry cells and/or in linked tables such astables related by one or more indices in a relational database. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which data entries in a database may store,retrieve, organize, and/or reflect data and/or records as used herein,as well as categories and/or populations of data consistently with thisdisclosure. Database may be a biological sample database. One or moretables contained within a biological sample database may include asensor data table. Sensor data table may include one or more biologicaldate markers obtained from at least a sensor 108. For instance, andwithout limitation, sensor data table may include menstrual cyclehistory of a user recorded by at least a sensor 108.

Continuing to refer to FIG. 1 , in some embodiments, computing device112 may be configured to generate a 3D pelvic measurement using the atleast a sensor 108, such as the strain gauge sensor described furtherabove. In some embodiments, computing device 112 may be furtherconfigured to measure a user's girth, such as user's belly girth. In anonlimiting example, computing device 112 may generate a 3D pelvic modelby generating a three-dimensional graphical representation of thedeformations in a flexible electrically conductive material attached tothe strain gauge sensor. Persons skilled in art, upon reviewing thisdisclosure, will recognize the many methods that can be used to generatethe 3D pelvic, and/or girth measurements, using the at least a sensor108.

Still referring to FIG. 1 , computing device 112 includes a detectionmodule 116 configured to extract at least a biological sample from theuser. Detection module 116 may be part of computing device 112. In anembodiment, detection module 116 may be a separate computing devicecommunicatively connected to computing device 112. In a furtherembodiment, detection module 116 may be a separate computing devicecommunicatively connected to at least a sensor 108. In an embodiment,extraction of biological includes biological samples passively gatheredby at least a sensor 108, such as through touching the user's skin,touching particles of sweat, particles of urine and the like. “At leasta biological sample,” as used in this disclosure, includes any sampleobtained from a human body of a user. At least a biological sample 120may be obtained from a bodily fluid and/or tissue such as sweat, blood,skin tissue, stool sample, hair, urine, and the like. At least abiological sample 120 may be obtained by detection module 116 from atleast a sensor 108 in contact with a human body of the user, such assensor 108 contact with user's skin, sweat absorbed by liner 104 fromthe user and the like. In a nonlimiting example, in a moment of stressthe user may sweat at an elevated rate, the liner 104 may absorb thatsweat and detection module 116 may detect the biological sample fromuser through sensor 108 embedded in the liner 104. In anothernonlimiting example, biological sample may be a vaginal discharge, whichdetection module 116 may extract by absorbing the sample through liner104. In a nonlimiting example, detection module 116 may extract a userskin tissue sample through sensor 108 direct contact with user's skin.In some embodiments, detection module 116 may be configured to use aspectroscopic method. In an embodiment, detection module 116 may beconfigured to use a colorimetric method. In some embodiments, andwithout limitation, detection module 116 may be configured to use anydetection method. Persons skilled in the art, upon reviewing thisdisclosure, will recognize the plurality of detection methodologies thatdetection module 116 may be configured to use.

Still referring to FIG. 1 , another property of a bodily fluid thatdetection module 116 detects may include indication of a presence of adate rape drug in the bodily fluid. As used in this disclosure, a “daterape drug” refers to a drug, usually given involuntarily to the user,drug that causes temporary loss of memory or inhibition, surreptitiouslygiven to someone in order to facilitate rape or sexual abuse. Types ofdate rape drugs include, but without limitation, gamma-hydroxybutyricacid (GHB), flunitrazepam also known as Rohypnol, ketamine, alcohol,marijuana, or any drug that inhibits a person. Though these drugs arenot exclusively used for such purposes but are the properties orside-effects of substances normally used for legitimate medicalpurposes. Sensors in detection device 112 may be configured to detectthe presence of a date rape drug in any bodily fluid that interacts withliner 108. Plurality of sensors may be configured to specifically detectflunitrazepam. Furthermore, detection device 112 may transmit an alertto any device described herein of the presence of such drugs in the bodyof the user or may use an agent to show its presence as described above.

Still referring to FIG. 1 , detection module 116 is configured toauthenticate the user as a function of the biological sample 120 andbiological data 124 of the user. In an embodiment, authentication may bea biometric authentication. A “biometric authentication,” as usedherein, is a characteristic of the user that verifies the user'sidentity. Detection module 116 may not be able to perform any of thestep as described herein until it receives biometric authentication.Biometric authentication may be detected by detection module 116 or maybe inputted by a user into any of the devices described herein.Biometric authentication may include, for example, scanning a userfingerprint, scanning an iris, taking a blood sample, pH level, and/ormeasuring the gait of a user. Biometric authentication may ensure thatany of the devices described herein are being used by the owner of userdevice, the user. In an embodiment, biometric authentication may beunimodal whereby only one biometric authentication is performed, orbiometric authentication may be multimodal whereby two or more biometricauthentications are performed. For example, a multimodal authenticationmay include a fingerprint scan and a blood sample. In an embodiment,multimodal authentication may be simultaneous, whereby two or morebiometric authentications are occurring at the same time, or multimodalauthentication may be performed in succession, whereby one biometricauthentication is performed followed in succession by at least a secondbiometric authentication. In an embodiment, biological data 124 is storein a biological sample database related to the user. Biological sampledatabase is described in further detail in FIG. 3 further below.Detection module 116 may compare biological sample 120 of user tobiological data 124 to identify the user that the biological sample 120belongs to. In some embodiments, detection module 116 may only detect acondition datum if the user that biological sample 120 belongs to isauthorized to use the garment embedded secretion analysis apparatus 100.In an embodiment, a plurality of users may be associated and authorizedto use garment embedded secretion analysis apparatus 100. Detectionmodule 116 may be configured to update biological data 124. In anonlimiting example, detection module 116 may update the user'sbiological data 124 with the biological sample 120 extracted from theuser.

Continuing to refer to FIG. 1 , computing device 112 may further includean activation device. As used herein, an “activation device” is a deviceconfigured to initiate a reaction to something. In an embodiment, anactivation device on garment embedded secretion analysis apparatus 100may activate the detection module 116. Activation device may alsoinitiate safety module 128 to contact the user or another device ororganization. In an embodiment, safety module 128 may generate an alertdatum as a function of the activation device. Moreover, activationdevice may be any sort of device that initiate and ceases actions, suchas an on/off switch, a button, a joystick, a pressure switch, atemperature switch, or the like. Activation device may be located on thewaistband of the underwear garment, or anywhere else that can be easilyaccessible to the user. In a nonlimiting example, safety module 128 maygenerate an alert datum and transmit the alert to emergency servicesbased on the user activating the activation device, such as pressing abutton on the device.

With continued reference to FIG. 1 , detection module 116 is configuredto detect a condition datum as a function of the at least a biologicalsample 120 and biological data 124. In a nonlimiting example, detectionmodule 116 may detect condition datum by comparing the at least abiological sample 120 of the user to the biological data 124 of theuser, such as comparing presence of blood in urine particles tobiological data of the user that includes information related to user'songoing kidney condition, in which case detection module 116 woulddetect that blood in urine is a condition related to user's kidneycondition. Detection module 116 may detect an event that indicates athreat to a user and transmit it to safety module 128. As used in thisdisclosure, “condition datum” describes a condition associated with thebiological sample 120 from the user. In a nonlimiting example, eventdatum may include a description signaling that an unknown chemical wasdetected in user's biological sample 120. In another nonlimitingexample, condition datum may include the detection of blood in thebiological sample 120. In another example, without limitations,condition datum may include a spike in user's body temperature.Detection module 116 may be configured to detect a condition datum as afunction a user data. In another nonlimiting example, biological sample120 may include blood in urine of a user going through her menstrualcycle, in this case detection module 116 may not detect a conditiondatum after comparing biological sample 120 to biological data 124 ofthe user since the presence of blood in urine at the level present inthe sample would be within an expected threshold.

Continuing to refer to FIG. 1 , user data may include one or moreevaluations of sensory ability, including measures of audition, vision,olfaction, gustation, vestibular function and pain. User data mayinclude genomic data. User data may include data concerning a microbiomeof a person, which, as used herein, includes any data describing anymicroorganism and/or combination of microorganisms living on or within aperson, including without limitation biomarkers, genomic data, proteomicdata, and/or any other metabolic or biochemical data useful for analysisof the effect of such microorganisms on other user data of a person,and/or on at least a prognosis and/or ameliorative processes.

With continued reference to FIG. 1 , user data may further includeinformation concerning the user's fertility and flow of their menstrualcycle. As used herein, “fertility” refers to capability to produceoffspring through reproduction following the onset of sexual maturity,while “flow” is the intensity of blood loss while the user experiences amenstrual cycle. User data may include any information about the user'sfertility and flow history, when the user is most likely to be able toget pregnant, if the user is pregnant, the user's fertility window, theuser's next predicted menstrual cycle, what day of their menstrual cyclethe user is on, if the user's menstrual cycle has bled through thegarment, density of the user's menstrual blood, which phase of theirmenstrual cycle the user is experiencing, and any other informationrelevant to the user's fertility or uterine health. In an embodiment,user data may include information relating to a user's diet. This mayinclude, without limitation, any data concerning what the user eats, ifthe user has any food allergies or sensitivities, the user's body massindex, which nutrients the user is lacking, or any other data concerningwhat the user has consumed. Also, user data may include informationrelating to a user's mental health. This may include any data that maybe detected relating to the mental health history or current mentalhealth state of the user. In a nonlimiting example, detection module 116may determine a condition datum of an unexpected presence of bloodoutside a user's menstrual cycle. In another nonlimiting example,detection module 116 may detect a condition datum of a prolongedelevated level of sweating in a user that has diabetes.

Referring still to FIG. 1 , computing device 112 may include a datainference engine configured to process data from detection module 116.In an embodiment, detection module 116 may be configured to determineevent datum as a function of the data inference engine. As used herein,a “data inference engine” is a database engine software component thatmakes a decision from the data contained in the database of apparatus100 or the algorithm derived from a deep learning AI system. Datainference engine may be transmitted data detected from at least a sensor108. Data may be any of the data described above or herein. Datainference engine takes this data from detection module 116 and deducesnew information. Such new information may consist of, withoutlimitation, deducing the user's next menstrual cycle, determining if theuser's body temperature is medically concerning, sending an alert toanother device, deciding that the blood alcohol content of the user isconcerningly high, to anything that a device may need to infer usingdata received. Data inference engine may work primarily using forwardchaining or backward chaining. “Forward chaining” is a mode that startswith the available data and uses inference rules to extract more datauntil a goal is reached, while “backward chaining” is another mode thatworks backward from the goal. Data inference engine may be cloud-based.As used herein, “cloud-based” may refer to stored, managed, andprocessed on a network of remote servers hosted on the internet, ratherthan on local servers or personal computers. Data inference engine mayalso use local servers or personal computers. Data inference engine mayalso be available via a WIFI network. Data inference engine may use amachine-learning model, neural network, classifier, or any other AIarchitecture as described herein. In an embodiment, data inferenceengine may be configured to receive and/or analyze data from one or morewearable devices to improve any health, wellness, and/or safety index. A“wearable device” as used in this disclosure, is any electronic devicethat may be worn by a user as an accessory, embedded into clothing,implanted into a user's body, and/or tattooed onto a user's skin. In anembodiment, detection module 116 may determine a condition datum as afunction of data inference engine. Data inference engine may include amachine-learning model trained to correlate a measurement from detectionmodule 116 to a user. The data may include, but not limited to howfertile the user is at the time of the measurement, a current orpotential disease state of a user, the level of libido, a user's bodychemistry level, or the like. Data inference engine may performdeterminations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses a body of data known as“training data” and/or a “training set” to generate an algorithm thatwill be performed by a computing device/module to produce outputs givendata provided as inputs; this is in contrast to a non-machine learningsoftware program where the commands to be executed are determined inadvance by a user and written in a programming language. Data inferenceengine may be designed and configured to create a machine learning modelconsistent with any machine-learning model described herein. Trainingdata and machine learning models/algorithms are described in more detailfurther below in FIGS. 4-7 .

Still referring to FIG. 1 , detection module 116 is configured todetermine an event datum as a function of the condition datum. An “eventdatum,” as used herein, is a description of an event that may impact theuser's safety and/or health. In a nonlimiting example, detection module116 may detect the presence of the chemical associated with rape drugsin biological sample 120, such as gamma-hydroxybutyric acid (GHB), andmay determine that the user has been drugged as an event datum. Inanother nonlimiting example, detection module 116 may determine that thediabetic user is hypoglycemic based on a condition datum of prolongedelevated sweating. In another nonlimiting example, detection module 116may detect blood in urine of a user with an ongoing kidney disease, andmay determine an event datum that describes the blood in urine as apossible kidney infection. In an embodiment, event datum may bedetermined as a function of a machine learning model. Machine learningmodel may receive elements in a biological sample as inputs and outputat least an event datum. In embodiments, machine-learning model mayutilize training data, where training data may include correlations ofsample data for elements in a biological sample to at least an eventdatum. In a nonlimiting example, training data may include a correlationof heart rates with user movements to a possible event datum. Such as,and without limitation, a level of increase in heart rate and a measuredamount of user movement may be correlated to possible physiologicallystressful situations, where a high level of increase in heart rate whileonly a minimum level of user movement is detected may mean that the useris undergoing a stressful event. Training data may include sample data.Training data may include data inputted by the user. Training data mayinclude past correlations of machine-learning model. Machine learningmodel may be trained by computing device 112 and/or a remote device.Training data and machine learning models/algorithms are described inmore detail further below in FIGS. 4-7 .

With continued reference to FIG. 1 , in a nonlimiting example, detectionmodule 116, using a machine-learning model, may be configured tocorrelate the detection of blood in biological sample 120 with a user'smenstrual cycle and generate an event datum if blood is detected whenuser is not on her period. Machine-learning model may be trained,without limitations, using training data correlating blood biologicalsamples 120 for a plurality of users with a plurality of stages of amenstrual cycle, where outputs may be determined based the quantity ofblood detected, the age of the user, hygiene methods, and the like.Training data and machine learning model may be consistent with, orinclude, any training data and/or machine learning model/algorithmdescribed throughout this disclosure.

With continued reference to FIG. 1 , in another nonlimiting example,detection module 116, using a machine-learning model, may be configuredto correlate chemical elements detected in biological sample 120 topossible incapacitating intoxication, and generate an event datum ifchemical is determined to possibly incapacitate the user, such as rapedrugs. Machine-learning model may be trained, without limitations, usingtraining data correlating chemical elements, quantities of the element,medical uses of chemical, such as prescription drugs, to incapacitatingscenarios, where outputs may be determined based on quantity of elementthat may cause incapacitation, whether detection is a discharge fromprior medical use, possible natural occurrence of the detected elementin the body, sample scenarios of incapacitating use of the element, andthe like. Training data and machine learning model may be consistentwith, or include, any training data and/or machine learningmodel/algorithm described throughout this disclosure.

Continuing to refer to FIG. 1 , computing device 112 includes a safetymodule 128 configured to receive event datum. Safety module 128 may bepart of computing device 112. In an embodiment, safety module 128 may bea separate computing device consistent with any computing devicesdescribed herein. In some embodiments, safety module 128 may be aseparate computing device communicatively connected to computing device112. As described herein, a “safety module” is a piece of technologythat helps eliminate or notify user, or others, of a threat to the user.

Still referring to FIG. 1 , safety module 128 is configured to generatean alert datum as a function of the event datum. An “alert datum,” asused in this disclosure, is data, or an element of data, that describes,or signals, a possible threatening or harmful situation. In anembodiment, alert datum may be generated in textual form. In anembodiment, alert datum may include an alert category. An “alertcategory,” as used herein, is a level of emergency related to the alertdatum, which may include, without limitations, a non-emergency category,a possible emergency category, an immediate emergency category, and thelike. In a nonlimiting example, alert datum may have a non-emergencycategory, such as an alert recommending that the user see a gynecologistbased on the presence of yeast infection on biological sample 120. Inanother nonlimiting example, alert datum may be an emergency category,such as if the user is unconscious and an alert is sent out to multipleuser devices alerting of the emergency. In some embodiments, alert datummay be an audio signal. In embodiments, alert datum may be a visualrepresentation configured to be displayed through a GUI. In someembodiments, alert datum may be vibration signal. In a nonlimitingexample, and following the example above, after detection module 116determines that user may have possibly been drugged, safety module 128may generate an alert datum describing the presence of a rape drug inthe user's system. In another nonlimiting example, alert datum may be asound alert, where a computing device may emit a high pitch sound whenalert datum describes a possible emergency, such as the user becomingunconscious. In some embodiments, safety module 128 may notify or alertemergency services of a threat. In other nonlimiting examples, alertdatum may include a textual alert notifying a diabetic user of lowglucose levels detected and an accompanying vibration signal as to grabuser's attention to the textual description of the alert datum. In afurther example, without limitations, when detection module 116continues to detect lowering levels of glucose after first alert datumis generated, safety module 128 may generate an audio signal alertingthe user, and possibly other people near the user, of the possiblydangerous lower levels of glucose detected. A “threat” to a user isanything that may cause damage or danger to the user. Safety module 128may be configured to notify the user of the presence of a threat. Athreat to the user may include, without limitation, a gender-basedviolence, a concerning level of drugs or alcohol in the user's system,losing too much blood, or anything else that may endanger the user.Safety module 128 may be enabled by the user in case of a threat to theuser, wherein safety module 128 may only contact others as a result of auser input granting safety module 128 permission to be enabled.

Still referring to FIG. 1 , in a nonlimiting example, safety module 128,using a machine-learning model, may be configured to correlate presenceof a chemical associated with rape drugs to a possible immediateincapacitation, where safety module 128 may generate an emergencycategory alert datum. Machine-learning model may be trained, withoutlimitations, using training data correlating chemical elements toeffects in a user's body, where output may be determined based on typeof chemical element detected, extent of effect in a user's body, amountof time chemical detected takes to incapacitate user, and the like.

With continued reference to FIG. 1 , in another nonlimiting example,safety module 128, using a machine-learning model, may be configured tocorrelate a detected yeast infection with possible complications relatedto the presence of the fungus in biological sample 120, where safetymodule 128 may generate a non-emergency category, such as an alertadvising user to take an antifungal medication, or an emergency categoryalert datum, such as advising user to seek immediate help or sending analert to user's gynecologist, based on correlation. Machine-learningmodel may be trained, without limitations, using training datacorrelating amounts of fungus detected with number of times fungus havebeen continuously detected, where outputs may be determined based onsample quantities of fungus detection, rate of increase of fungusculture detected over a period of time, hygiene methods used by users,and the like.

Still referring to FIG. 1 , in an example, without limitations, safetymodule 128, using a machine-learning model, may be configured tocorrelate a spike in heart rate and user's movements with GPS locationof the user, where safety module 128 may generate an alert datum,without limitations, with a “possible emergency” category, such asinforming another user of user's location, an “immediate emergency”category, such as sending user's location and a description of theemergency to emergency services, or a non-emergency category, such asadvising user to avoid staying in the detected location due to safetyrisks. Machine-learning model may be trained, without limitations, usingtraining data correlating levels of heart rate increase and levels ofuser movements to locations marked as unsafe or possibly unsafedepending on time of day, where outputs may be determined based onsample user fitness levels, possible situations justifying a spike inheart rate without correlated user movements, location, times of daywhere location is considered unsafe, past outputs near detected locationfor other users, and the like.

Continuing to refer to FIG. 1 , in an embodiment, safety module 128 maybe configured to determine alert datum as a function of the event datumand the data inference engine. Data inference engine may include amachine-learning model trained to correlate event datum to an alertdatum. In an embodiment, machine-learning model may utilize trainingdata correlation sample event datum outputs, or previous outputs for theuser, to sample data that includes situations or harmful events that maycorrelate to the event data. In a nonlimiting example, training data mayinclude an event datum describing the detection of fungus is a vaginaldischarge biological sample correlated to a possible vaginal yeastinfection. In another nonlimiting example, training data may includeevent datum describing a possible physiologically stressful situation,such as when sudden spike in heart rate is detected while minimum usermovement is detected, correlated to high crime areas. In this example,without limitations, safety module 128 may generate an alert datumdescribing a possible threat to the user, such as a robbery. Trainingdata may include sample data. Training data may include data inputted bythe user. Training data may include past correlations ofmachine-learning model. Machine learning model may be trained bycomputing device 112 and/or a remote device. Training data and machinelearning models/algorithms are described in more detail further below inFIGS. 4-7 .

Still referring to FIG. 1 , computing device 112 may be configured togenerate a course of action output as a function of the alert datum. A“course of action output,” as described herein, is a set of instructionthat the user may follow based on the alert datum generated. In anembodiment, course of action may be transmitted to another user, or aplurality of user. In an embodiment, course of action output may begenerated as a function of a machine-learning model. In embodiments,machine learning model may be configured to receive alert datum andother data generated by at least a sensor 108 and output a course ofaction. In a nonlimiting example, machine learning model may take a analert datum describing a physiological stress and a GPS location of theuser as inputs and may generate an output describing the location of thenearest business, or other populated area, where user can seek help. Inan embodiment, course of action output machine learning model may betrained using training data. Training data may include past alert datumsgenerated at a specific area, which may be one or more GPS locations ora geographical area, correlated to past course of action outputs, whichmay include previous outputs from machine-learning model. In anonlimiting example, high stress alerts that have been detected in acertain area may be correlated to known populated addresses within thatarea, such as a list of businesses in that area. In an embodiment, anumber of alert datums detected over a period of time may be correlatedto a course of action output suggesting that user seek professionalhelp. For example, and without limitations, the generation of multiplealert datums over a period of days for an yeast infection may causecomputing device 112 to generate a course of action output suggestingthat user seek a gynecologist, such as when fungal growth may be growingat a rate where antifungal medication is not enough and antibiotics maybe required. Training data may include sample data. Training data mayinclude data inputted by the user. Training data may include pastcorrelations of machine-learning model. Machine learning model may betrained by computing device 112 and/or a remote device. Training dataand machine learning models/algorithms are described in more detailfurther below in FIGS. 4-7 .

With continued reference to FIG. 1 , computing device 112 may beconfigured to use a communication protocol. A “communication protocol,”as used herein, is a system of rules that allows to devices tocommunicate and transmit information. Communication protocol mayinclude, without limitation, rules, synchronization, syntax, andsemantics of communication between the devices. Communication protocolmay include any of the hardware or software as described herein.Communication protocol may be included in safety device 116 or any otherdevice as described herein. Communication protocol may includenear-field communication (NFC) or radio frequency identification (RFID).Communication protocol may include, including without limitationinternet protocol (IP), controller area network (CAN) protocols, serialcommunication protocols (e.g., universal asynchronousreceiver-transmitter [UART]), parallel communication protocols (e.g.,printer port IEEE 128), and the like.

Still referring to FIG. 1 , safety module 128 may be configured totransmit the alert datum of a threat to user device 132. In anembodiment, transmitting an alert datum to user device 132 may includecontacting emergency services in case of a threat to a user. As usedherein, “emergency services” refer to rescue services that ensure publicsafety and health, such as the police, fire department, medicalservices, or any other department that can provide help to the user. Inan embodiment, transmitting an alert datum to user device 132 mayinclude notifying family members, or any other person configured by theuser. In an embodiment, user device 132 may include a wearable deviceworn by the user. In an embodiment, alert datum that may be transmittedto user device 132 may be the potential presence of a date rape drug, asdescribed above. In another embodiment, another alert datum transmittedto user device 132 may be an alert notifying the user or others if theuser's blood alcohol content is reaching a medically concerning level,meaning that the level of alcohol in their system may affect theirhealth. Additionally, another example of a threat, without limitation,is any sort of abnormality in the health of the user, such as lowvitamin levels. In such cases, safety device may notify or alert theuser rather than emergency services since the safety of the person isnot in jeopardy. In an embodiment, safety module 128 may be configuredto send out the location of the user when activated using a globalpositioning system. In such situation, garment embedded secretionanalysis apparatus 100 is used to help prevent assault and protect theuser from being a victim. In another embodiment, user device 132 may bea computing device attached to a vehicle. In a further embodiment,computing device attached to a vehicle may be an ignition interlockingdevice. An “ignition interlocking device,” as used herein, is a deviceconnected to a vehicle that prevents the operation of the vehicle whendevice is activated. In a nonlimiting example, detection module 116detects the presence of alcohol at a level that is unsafe to operate avehicle, in such case safety module 128 may transmit an alert to theuser's vehicle preventing its operation until detection module 116detects an alcohol level in user's system that is a threshold for safelyoperating a vehicle.

Still referring to FIG. 1 , computing device 112 may further include aglobal positioning system. As used in this disclosure, a “globalpositioning system,” also known as GPS, is a satellite-based navigationsystem composing of satellites, ground stations, and receivers. Thesatellites circulate Earth and constantly are sending out signals, sothat the ground stations may use radar to make sure the satellites arelocated where they should be. The receiver, which in this case is thesafety device, wearable device, output device, or any other devicedescribed herein, is constantly searching for a signal from thesesatellites and figures out how far away they are from the satellite;this distance is then used to find the exact location of the receiver.Once the receiver calculates its distance from four or more GPSsatellites, it may be configured to figure out where the exact locationof the receiver is. GPS may calculate the latitude, longitude, andheight position of a user. Furthermore, safety device may include aglobal positioning system sensor to calculate distance or height. globalpositioning system sensor may be any of the sensors as described herein.

Continuing to refer to FIG. 1 , computing device 112 may also include areporting engine. In an embodiment, transmitting alter datum to userdevice 132 may include utilizing reporting engine. As used herein, a“reporting engine” is a database engine software component that isconfigured to receive data and report it to a specific device, person,organization, or the like. In an embodiment, reporting engine may beused to report data to a user, emergency services, or any other deviceor organization described herein. Reporting engine is configured toreport results to a user device 132 from the data inference engine. Asused herein, a “user device” is a device configured to displayinformation to someone or something. Interaction of user with a userdevice 132 may be through an input device. Examples of an input deviceinclude, but are not limited to, an alpha-numeric input device (e.g., akeyboard), a pointing device, a joystick, a gamepad, an audio inputdevice (e.g., a microphone, a voice response system, etc.), a cursorcontrol device (e.g., a mouse), a touchpad, an optical scanner, a videocapture device (e.g., a still camera, a video camera), a touchscreen, aninceptor stick, and any combinations thereof. Also, user device 132 mayinclude a wearable smartwatch, an activity tracker, a smartphone, amobile app. In non-limiting illustrative examples, wearable device datamay include without limitation accelerometer data, pedometer data,gyroscope data, electrocardiography (ECG) data, electrooculography (EOG)data, bioimpedance data, blood pressure and heart rate monitoring,oxygenation data, biosensors, fitness trackers, force monitors, and thelike, as described above. User device 132 may receive input from user,emergency services, family members, etc. through standard I/O interfacesuch as ISA (Industry Standard Architecture), PCI (Peripheral ComponentInterconnect) Bus, and the like. Output device may receive input fromuser through standard I/O operation. In one embodiment, user device 132may further receive input from user through optical tracking of motion.In one embodiment, user device 132 may further receive input from userthrough voice-commands. User device 132 may further use event-drivenprogramming, where event listeners are used to detect input from userand trigger actions based on the input.

Continuing to refer to FIG. 1 , reporting engine includes a dashboard. A“dashboard” is a graphical user interface facing a user that may containinstruments and/or controls. Dashboard may include user demographicdata, the particular measurement from a sensor, the state, contactinformation for the user's healthcare practitioner, data comparing theuser to other users of the same gender, age group, suggestion forimproving the user's health (diet and exercise suggestions), suggestionsfor increasing the user's libido, or suggestions for improving the user'general well-being. Any of the data described herein may be displayed indashboard.

Now referring to FIGS. 2A and 2B, a front view and a back view of anexemplary embodiment of a secretion analysis apparatus 100 embedded inan underwear garment is presented. Liner 104 may cover just a portion ofthe underwear garment as seen in the figure or may be a lined throughoutthe inside the entire underwear garment. In a nonlimiting example,garment may be an underwear. In another nonlimiting example, garment maybe a pair of socks.

Now referring to FIG. 2C, a front view of an exemplary embodiment of asecretion analysis apparatus 100 embedded in a t-shirt garment isillustrated. Liner 104 may be embedded in any portion of the t-shirtgarment that is in direct contact with the user. Without limitation,garment embedded secretion analysis apparatus 100 may be embedded in anygarment that is configured to be in direct contact with the user. In anonlimiting example, garment may be a shirt. In another example, andwithout limitations, garment may be a pair of pants.

Now referring to FIG. 2D, an exemplary embodiment of a liner 104 isshown. Liner 104 may be any size or shape as long as it fits within agarment that secretion analysis apparatus 100 is embedded in and is ableto perform all the steps as described herein.

Now referring to FIG. 3 , an exemplary embodiment of a biological sampledatabase 300 is presented. As a non-limiting example, one or moreelements of biological data may be stored in and/or retrieved from abiological sample database 300. “Biological data,” includes data relatedto user data, biological samples associated with the user, and the like.Biological sample database 300 may include any data structure forordered storage and retrieval of data, which may be implemented as ahardware or software module. A biological sample database 300 may beimplemented, without limitation, as a relational database, a key-valueretrieval datastore such as a NOSQL database, or any other format orstructure for use as a datastore that a person skilled in the art wouldrecognize as suitable upon review of the entirety of this disclosure. Abiological sample database 300 may include a plurality of data entriesand/or records corresponding to elements of biological data as describedabove. Data entries and/or records may describe, without limitation,data concerning particular biological samples that have been collected;entries may describe reasons for collection of samples, such as withoutlimitation one or more conditions being tested for. Data entries mayinclude prognostic labels and/or other descriptive entries describingresults of evaluation of past biological samples, including diagnosesthat were associated with such samples, prognoses and/or conclusionsregarding likelihood of future diagnoses that were associated with suchsamples, and/or other medical or diagnostic conclusions that werederived. Such conclusions may have been generated by apparatus 100 inprevious iterations of methods, with or without validation ofcorrectness by medical professionals. Data entries in a biologicalsample database 300 may be flagged with or linked to one or moreadditional elements of information, which may be reflected in data entrycells and/or in linked tables such as tables related by one or moreindices in a relational database; one or more additional elements ofinformation may include data associating a biological sample and/or aperson from whom a biological sample was extracted or received with oneor more cohorts, including demographic groupings such as ethnicity, sex,age, income, geographical region, or the like, one or more commondiagnoses or physiological attributes shared with other persons havingbiological samples reflected in other data entries, or the like.Additional elements of information may include one or more categories ofbiological data as described above. Additional elements of informationmay include descriptions of particular methods used to obtain biologicalsamples, such as without limitation, capture of data with one or moresensors, and/or any other information concerning provenance and/orhistory of data acquisition. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various ways in whichdata entries in a biological sample database 300 may reflect categories,cohorts, and/or populations of data consistently with this disclosure.

With continued reference to FIG. 3 , biological sample database 300 mayinclude tables listing one or more samples according to sample source.For instance, and without limitation, biological sample database 300 mayinclude a fluid sample table 304 listing samples acquired from a personby extraction of fluids, such as without limitation blood, sweat, urineand the like. As another non-limiting example, biological sampledatabase 300 may include a sensor data table 308, which may list samplesacquired using one or more sensors, for instance as described in furtherdetail below. As a further non-limiting example, biological sampledatabase 300 may include a genetic sample table 312, which may listpartial or entire sequences of genetic material. Genetic material may beextracted and amplified, as a non-limiting example, using polymerasechain reactions (PCR) or the like. As a further example, alsonon-limiting, biological sample database 300 may include a medicalreport table 316, which may list textual descriptions of medical tests,including without limitation radiological tests or tests of strengthand/or dexterity or the like. Data in medical report table may be sortedand/or categorized using a language processing module 312, for instance,translating a textual description into a numerical value and a labelcorresponding to a category of physiological data; this may be performedusing any language processing algorithm or algorithms as referred to inthis disclosure. As another non-limiting example, biological sampledatabase 300 may include a tissue sample table 320, which may recordbiological samples obtained using tissue samples, such as a user'sdead-skin. Tables presented above are presented for exemplary purposesonly, persons skilled in the art will be aware of various ways in whichdata may be organized in biological sample database 300 consistentlywith this disclosure.

Referring now to FIG. 4 , an exemplary embodiment of a machine-learningmodule 400 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 404 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 408 given data provided as inputs 412;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 4 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 404 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 404 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 404 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 404 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 404 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 404 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data404 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 4 ,training data 404 may include one or more elements that are notcategorized; that is, training data 404 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 404 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 404 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 404 used by machine-learning module 400 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample training data may correlate a presence of blood in a urinesample to an inflammation of the kidneys.

Further referring to FIG. 4 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 416. Training data classifier 416 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 400 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 404. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data to a possiblepregnancy based on a person's sex and menstrual cycle.

Still referring to FIG. 4 , machine-learning module 400 may beconfigured to perform a lazy-learning process 420 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 404. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 404 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 4 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 424. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 424 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 424 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 404set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 4 , machine-learning algorithms may include atleast a supervised machine-learning process 428. At least a supervisedmachine-learning process 428, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude urine samples as described above as inputs, possible urinarydiseases as outputs, and a scoring function representing a desired formof relationship to be detected between inputs and outputs; scoringfunction may, for instance, seek to maximize the probability that agiven input and/or combination of elements inputs is associated with agiven output to minimize the probability that a given input is notassociated with a given output. Scoring function may be expressed as arisk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided intraining data 404. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various possiblevariations of at least a supervised machine-learning process 428 thatmay be used to determine relation between inputs and outputs. Supervisedmachine-learning processes may include classification algorithms asdefined above.

Further referring to FIG. 4 , machine learning processes may include atleast an unsupervised machine-learning processes 432. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 4 , machine-learning module 400 may be designedand configured to create a machine-learning model 424 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 4 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminant analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized trees, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 5 , an exemplary embodiment of neural network 500is illustrated. A neural network 500 also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes 504, one or more intermediate layers 508, and an output layer ofnodes 512. Connections between nodes may be created via the process of“training” the network, in which elements from a training dataset areapplied to the input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning. Connections may run solely from input nodes toward outputnodes in a “feed-forward” network, or may feed outputs of one layer backto inputs of the same or a different layer in a “recurrent network.” Asa further non-limiting example, a neural network may include aconvolutional neural network comprising an input layer of nodes, one ormore intermediate layers, and an output layer of nodes. A “convolutionalneural network,” as used in this disclosure, is a neural network inwhich at least one hidden layer is a convolutional layer that convolvesinputs to that layer with a subset of inputs known as a “kernel,” alongwith one or more additional layers such as pooling layers, fullyconnected layers, and the like.

Referring now to FIG. 6 , an exemplary embodiment of a node of a neuralnetwork is illustrated. A node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally, or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above.

Referring to FIG. 7 , an exemplary embodiment of fuzzy set comparison700 is illustrated. A first fuzzy set 704 may be represented, withoutlimitation, according to a first membership function 708 representing aprobability that an input falling on a first range of values 712 is amember of the first fuzzy set 704, where the first membership function708 has values on a range of probabilities such as without limitationthe interval [0,1], and an area beneath the first membership function708 may represent a set of values within first fuzzy set 704. Althoughfirst range of values 712 is illustrated for clarity in this exemplarydepiction as a range on a single number line or axis, first range ofvalues 712 may be defined on two or more dimensions, representing, forinstance, a Cartesian product between a plurality of ranges, curves,axes, spaces, dimensions, or the like. First membership function 708 mayinclude any suitable function mapping first range 712 to a probabilityinterval, including without limitation a triangular function defined bytwo linear elements such as line segments or planes that intersect at orbelow the top of the probability interval. As a non-limiting example,triangular membership function may be defined as:

${y\left( {x,a,b,c} \right)} = \left\{ \begin{matrix}{0,} & {{{for}\ x} > {c\ {and}\ x} < a} \\{\frac{x - a}{b - a},} & {{{for}\ a} \leq x < b} \\{\frac{c - x}{c - b},} & {{{if}\ b} < x \leq c}\end{matrix} \right.$

a trapezoidal membership function may be defined as:

${y\left( {x,a,b,c,d} \right)} = {\max\left( {{\min\ \left( {\frac{x - a}{b - a},1,\frac{d - x}{d - c}} \right)},\ 0} \right)}$

a sigmoidal function may be defined as:

${y\left( {x,a,c} \right)} = \frac{1}{1 - e^{- {a({x - c})}}}$

a Gaussian membership function may be defined as:

${y\left( {x,c,\sigma} \right)} = e^{{- \frac{1}{2}}{(\frac{x - c}{\sigma})}^{2}}$

and a bell membership function may be defined as:

${y\left( {x,a,b,c,} \right)} = \left\lbrack {1 + {❘\frac{x - c}{a}❘}^{2b}} \right\rbrack^{- 1}$

Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various alternative or additionalmembership functions that may be used consistently with this disclosure.

Still referring to FIG. 7 , first fuzzy set 704 may represent any valueor combination of values as described above, including output from oneor more machine-learning models and condition datum determined frombiological samples from sensor 108, a predetermined class, such aswithout limitation previous user data. A second fuzzy set 716, which mayrepresent any value which may be represented by first fuzzy set 704, maybe defined by a second membership function 720 on a second range 724;second range 724 may be identical and/or overlap with first range 712and/or may be combined with first range via Cartesian product or thelike to generate a mapping permitting evaluation overlap of first fuzzyset 704 and second fuzzy set 716. Where first fuzzy set 704 and secondfuzzy set 716 have a region 728 that overlaps, first membership function708 and second membership function 720 may intersect at a point 732representing a probability, as defined on probability interval, of amatch between first fuzzy set 704 and second fuzzy set 716.Alternatively, or additionally, a single value of first and/or secondfuzzy set may be located at a locus 736 on first range 712 and/or secondrange 724, where a probability of membership may be taken by evaluationof first membership function 708 and/or second membership function 720at that range point. A probability at 728 and/or 732 may be compared toa threshold 740 to determine whether a positive match is indicated.Threshold 740 may, in a non-limiting example, represent a degree ofmatch between first fuzzy set 704 and second fuzzy set 716, and/orsingle values therein with each other or with either set, which issufficient for purposes of the matching process; for instance, thresholdmay indicate a sufficient degree of overlap between an output from oneor more machine-learning models and/or a condition datum and apredetermined class, such as without limitation a user state, forcombination to occur as described above. Alternatively, or additionally,each threshold may be tuned by a machine-learning and/or statisticalprocess, for instance and without limitation as described in furtherdetail below.

Further referring to FIG. 7 , in an embodiment, a degree of matchbetween fuzzy sets may be used to classify a condition datum withprevious user data. For instance, if a condition datum has a fuzzy setmatching element of user data fuzzy set by having a degree of overlapexceeding a threshold, computing device 104 may classify the conditiondatum as belonging to the element of user data. Where multiple fuzzymatches are performed, degrees of match for each respective fuzzy setmay be computed and aggregated through, for instance, addition,averaging, or the like, to determine an overall degree of match.

Still referring to FIG. 7 , in an embodiment, a condition datum may becompared to multiple user data fuzzy sets. For instance, condition datummay be represented by a fuzzy set that is compared to each of themultiple element of user data fuzzy sets; and a degree of overlapexceeding a threshold between the condition datum fuzzy set and any ofthe multiple element of user data fuzzy sets may cause computing device112 to classify the condition datum as belonging to an element of userdata. For instance, in one embodiment there may be two element of userdata fuzzy sets, representing respectively a first element of user dataand a second element of user data. First element of user data may have afirst element of user data fuzzy set; second element of user data mayhave a second element of user data fuzzy set; and condition datum mayhave a condition datum fuzzy set. Computing device 112, for example, maycompare a condition datum fuzzy set with each of first element of userdata fuzzy set and second element of user data fuzzy set, as describedabove, and classify a condition datum to either, both, or neither offirst or second element of user data. Machine-learning methods asdescribed throughout may, in a non-limiting example, generatecoefficients used in fuzzy set equations as described above, such aswithout limitation x, c, and σ of a Gaussian set as described above, asoutputs of machine-learning methods. Likewise, condition datum may beused indirectly to determine a fuzzy set, as condition datum fuzzy setmay be derived from outputs of one or more machine-learning models thattake the condition datum directly or indirectly as inputs.

Now referring to FIG. 8 , a flow diagram illustrating an exemplaryembodiment of a method 800 of manufacturing for a garment embeddedsecretion analysis apparatus 100. Garment embedded secretion analysisapparatus 100 may be configured to be worn on a body of a user. At step805, method 800 includes collecting a fabric to comprise a liner 104 forthe garment. Liner 104 may comprise an absorbent material to capturebodily fluids. Fabric may include cotton, polyester, polyamide,elastane, or any of the materials described herein. Fabric of the linermay be a different or the same material used for other aspects ofgarment embedded secretion analysis apparatus 100. Fabric may be any ofthe fabric as described herein with reference to FIG. 1 . Liner 104 isany of the liners as described herein with reference to FIGS. 1, 2A, 2B,2C and 2D.

Referring still to FIG. 8 , at step 810, method 800 includes weavingconductive yarn into the fabric of the liner 104. As used herein,“conductive yarn” spun thread that is able to conduct an electricalcurrent. Conductive yarn may be woven, or interlaced, into liner 104using a plain weave, basket weave, twill weave, satin weave, or anyother method of connecting fabric together. Conductive yarn may be usedto help alleviate or dissipate static electricity or friction betweenthe garment and the kin of the user or other garments. Furthermore,conductive yarn includes a conductive polymer. As used herein, a“conductive polymer” is a substance or material consisting of very largemolecules that can conduct an electrical current. Conductive polymersmay include, without limitation, polyacetylene (PA), polyaniline (PANT),polypyrrole (PPy), polythiophene (PTH), poly(para-phenylene) (PPP),poly(phenylenevinylene) (PPV), and polyfuran (PF). In one embodiment,method 700 may include weaving a yarn-shaped battery into the fabric ofthe liner. In a further embodiment, the yarn-shaped battery may be alithium-ion battery. Conductive yarn may be any of the conductive yarnsor fabrics as described herein throughout. Liner 104 is any of theliners as described herein with reference to FIGS. 1, 2A, 2B, 2C, and2D.

Still referring to FIG. 8 , at step 815, method 800 includes embeddingat least a sensor 108 into the fabric of the liner 104. As used herein,“embedding” means to fix firmly and deeply in a surrounding mass, whichin this case is liner 104. Embedding may involve communicativelyconnecting. At least a sensor 108 may be any sensor as described hereinwith reference to FIGS. 1 and 2D. Liner 108 is any of the liners asdescribed herein with reference to FIGS. 1, 2A, 2B, 2C and 2D.

Continuing to refer to FIG. 8 , at step 820, method 800 includesinstalling a computing device 112 into the liner 104. As used herein,“installing” refers to fixing something so it is ready for use.Computing device 112 and/or detection module 116 is configured tomeasure body temperature. Computing device 112 and/or detection module116 may be configured to measure a property of a bodily fluid. Propertyof a bodily fluid may include the presence of a date rape drug in a bodyof a user. Computing device 112 may include a global positioning system.Computing device 112 may include an activation device, and theactivation device may be activated by a biometric authentication.Computing device 112 may include a GPS location feature. Computingdevice 112 includes a communication protocol. Computing device 112and/or safety module 128 may be enabled to contact emergency services incase of a threat to a user. Installing a computing device into thefabric of the liner includes embedding wires from these devices into theliner to make them invisible to the user. Computing device 112 mayinclude any of the detection devices as described herein with referenceto FIGS. 1 and 2D. Liner 104 is any of the liners as described hereinwith reference to FIGS. 1, 2A, 2B, 2C and 2D.

Still referring to FIG. 8 , method of manufacturing 800 may furtherinclude sewing the fabric of the liner to the fabric of the garment.Once all component needed are embedded and installed into liner 104, itmay need to be attached to another piece of fabric comprising the restof the garment. Liner 104 is any of the liners as described herein withreference to FIGS. 1, 2A, 2B, 2C and 2D. Garment embedded secretionanalysis apparatus 100 may be any of the systems described hereinthroughout.

Still referring to FIG. 8 , a computing device 112 may be configured todetect a temperature datum using detection module 116 and transmit thetemperature datum to the user device 132. Computing device 112 mayfurther be configured to detect a fluid datum using detection module 116and transmit the fluid datum to the user device 132. Furthermore,computing device 112 may be configured to determine a threat, referredherein as event datum, as a function of the fluid datum, and contactemergency services as a function of the detected threat. Computingdevice 112 may include data inference engine and reporting engine. Datainference engine may include a machine-learning model trained tocorrelate a measurement from the detection module to a state of a user.Reporting engine 128 may include a dashboard.

Now referring to FIG. 9 , flow diagram illustrating a garment embeddedsecretion analysis method 900 is presented. At step 905, method 900includes extracting at least a biological sample from a user. In anembodiment, extraction may refer to the passive collection of biologicalsample 120 by at least a sensor 108, such as the direct contact withuser's skin or sweat particles. In an embodiment, extraction may referto the collection of biological sample 120 by absorption of sweat orurine particles by liner 104. This step may be implemented as disclosedwith reference to FIGS. 1-7 .

Continuing to refer to FIG. 9 , at step 910, method 900 includesauthenticating the user as a function of the at least a biologicalsample. In an embodiment, authentication may include biologicalauthentication, where biological sample 120 of user is compared tobiological data 124, such as a biological sample database 300,associated with the user. In a nonlimiting example, DNA sequence presentin biological sample 120 may be compared to DNA sequence of the user inbiological data 124, where the user may be authenticated if the DNAsequences are a match. Step 910 may be implemented as disclosed withreference to FIGS. 1-7 .

Still referring to FIG. 9 , at step 915, method 900 includes detecting acondition datum as a function of the at least a biological sample. In anembodiment, detection module 116 may detect a condition for the userbased on the biological sample 120 by comparing the sample to biologicaldata 124 belonging to the user, such as detecting low glucose levels inbiological sample 120 for a diabetic user, which detection module 116would detect a low glucose condition datum. This step may be implementedas disclosed with reference to FIGS. 1-7 .

With continued reference to FIG. 9 , at step 920, method 900 includesdetermining an event datum as a function of the condition datum. In anembodiment, detection module 116 may transmit event datum to a userdevice 132. In a nonlimiting example, an event datum describing a spikein body temperature may not rise to the level where an alert datum wouldbe generated, however user may still want to receive that information ina user device, such as a smartwatch. Step 920 may be implemented asdisclosed with reference to FIGS. 1-7 .

Still referring to FIG. 9 , at step 925, method 900 includes generatingan alert datum as a function of the event datum. In an embodiment, alertdatum may be generated and transmitted to a user device as a function ofan activation device, such as a user pushing a button. In anotherembodiment, activation device may be activated through a user'smovement, such as a specific set of motions. In embodiments, alert datummay include the users GPS location. In a nonlimiting example, a user mayfeel unsafe and may not have want to bring attention of a possibleattacker by pulling out a phone, in that situation user may press abutton on activation device embedded in a piece of user's clothes, suchas a t-shirt, which may cause safety module 128 to generate an alertwith user's GPS location and send it to emergency services, or a userdevice in user's emergency contact list. In an embodiment, safety module128 may transmit an alert datum to user's device. In an embodiment,safety module 128 may transmit alert datum to a second user device. Inembodiments, safety module 128 may transmit alert datum to user's deviceand to a second user device. This step may be implemented as disclosedwith reference to FIGS. 1-7 .

Continuing to refer to FIG. 9 . In an embodiment, method 900 may furtherinclude transmitting the event datum to a user device. In a nonlimitingexample, detection module 116 may determine that biological sample 120contains low level of glucose and may transmit the low glucose level toa user device, such as a smartwatch worn by the user. In anotherembodiment, detection modules 116 may determine a condition datum thatdescribes the presence of a drug commonly known as a rape drug, safetymodule 128 may then generate an alert datum describing the possibleinvoluntary intoxication and may further send an alert to a user devicebelonging to the user, and another user device associated with anemergency contact for the user and/or emergency services, such as a 911operator.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 10 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1000 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1000 includes a processor 1004 and a memory1008 that communicate with each other, and with other components, via abus 1012. Bus 1012 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 1004 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 1004 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 1004 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 1008 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1016 (BIOS), including basic routines thathelp to transfer information between elements within computer system1000, such as during start-up, may be stored in memory 1008. Memory 1008may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1020 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1008 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1000 may also include a storage device 1024. Examples ofa storage device (e.g., storage device 1024) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1024 may beconnected to bus 1012 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1024 (or one or more components thereof) may be removably interfacedwith computer system 1000 (e.g., via an external port connector (notshown)). Particularly, storage device 1024 and an associatedmachine-readable medium 1028 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1000. In one example,software 1020 may reside, completely or partially, withinmachine-readable medium 1028. In another example, software 1020 mayreside, completely or partially, within processor 1004.

Computer system 1000 may also include an input device 1032. In oneexample, a user of computer system 1000 may enter commands and/or otherinformation into computer system 1000 via input device 1032. Examples ofan input device 1032 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1032may be interfaced to bus 1012 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1012, and any combinations thereof. Input device 1032may include a touch screen interface that may be a part of or separatefrom display 1036, discussed further below. Input device 1032 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1000 via storage device 1024 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1040. A networkinterface device, such as network interface device 1040, may be utilizedfor connecting computer system 1000 to one or more of a variety ofnetworks, such as network 1044, and one or more remote devices 1048connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1044, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1020, etc.) may be communicated to and/or fromcomputer system 1000 via network interface device 1040.

Computer system 1000 may further include a video display adapter 1052for communicating a displayable image to a display device, such asdisplay device 1036. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1052 and display device 1036 maybe utilized in combination with processor 1004 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1000 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1012 via a peripheral interface 1056.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. An apparatus for a garment embedded secretion analysis, the apparatuscomprising: a liner comprising at least a sensor from a plurality ofsensors, wherein the liner is configured to be attached to a garmentworn by a user, wherein the at least a sensor is configured to extractat least a biological sample from the user; and a computing deviceembedded in the liner and communicatively connected to the at least asensor, the computing device comprising: a detection module configuredto: receive the at least a biological sample from the at least a sensor;authenticate the user as a function of the at least a biological sampleand biological data of the user; detect a condition datum as a functionof the at least a biological sample and biological data of the user; anddetermine an event datum as a function of the condition datum; and asafety module configured to: receive the event datum; and generate analert datum as a function of the event datum, wherein the alert datum isclassified into an alert category of a plurality of alert categories,wherein each alert category maps to a different level of emergency. 2.The apparatus of claim 1, wherein the computing device is furtherconfigured to authenticate the user as a function of a biologicalauthentication.
 3. The apparatus of claim 2, wherein the computingdevice is further configured to transmit the alert datum to a userdevice.
 4. The apparatus of claim 3, wherein the computing device isfurther configured to transmit the alert datum to a second user device.5. The apparatus of claim 2, wherein the alert datum is transmitted as afunction of a reporting engine.
 6. The apparatus of claim 4, wherein thesecond user device is an emergency service.
 7. The apparatus of claim 1,wherein the apparatus further comprises a battery embedded in the liner.8. The apparatus of claim 7, wherein the battery is a yarn-shapedbattery.
 9. The apparatus of claim 1, wherein the liner comprises anabsorbent material.
 10. The apparatus of claim 1, wherein the at least asensor comprises a sensor configured to measure body temperature. 11.The apparatus of claim 1, wherein the computing device further comprisesa global positioning system (GPS).
 12. The apparatus of claim 11,wherein the alert datum comprises the user's GPS location.
 13. Theapparatus of claim 1, wherein the safety module is further configured togenerate the alert datum as a function of a user motion.
 14. A methodfor a garment embedded secretion analysis, the method comprising:extracting, by a computing device communicatively connected to at leasta sensor embedded in a liner, at least a biological sample from a user;authenticating, by the computing device, the user as a function of thebiological sample and biological data of the user; detecting, by thecomputing device, a condition datum as a function of the biologicalsample and biological data of the user; determining, by the computingdevice, an event datum as a function of the condition datum; andgenerating, by the computing device, an alert datum as a function of theevent datum, wherein the alert datum is classified into an alertcategory of a plurality of alert categories, wherein each alert categorymaps to a different level of emergency.
 15. The method of claim 14,wherein the method further comprises transmitting, by the computingdevice, the event datum to a user device.
 16. The method of claim 15,wherein the method further comprises transmitting, by the computingdevice, the alert datum to the user device.
 17. The method of claim 16,wherein the method further comprises transmitting the alert datum to asecond user device.
 18. The method of claim 14, wherein the alert datumcomprises a GPS location of the user.
 19. A method of manufacturing agarment embedded secretion analysis system, wherein the method ofmanufacturing comprises: collecting a fabric to comprise a liner for thegarment; weaving conductive yarn into the fabric of the liner; embeddingat least a sensor from a plurality of sensors into the fabric of theliner, wherein the at least a sensor is configured to extract at least abiological sample from a user of the garment; and installing a computingdevice into the liner, wherein: the computing device is configured togenerate an alert datum as a function of the at least a biologicalsample; the alert datum is classified into an alert category of aplurality of alert categories; and each alert category maps to adifferent level of emergency.
 20. The method of claim 19, wherein themethod further comprises weaving a yarn-shaped battery into the fabricof the liner.